stock-valuation-monitor

股票和ETF估值监控工具，基于PE、PB BAND和历史百分位评估估值区间（机会/风险）

Security Scan

The skill's code, instructions, and declared dependencies are consistent with a stock/ETF valuation tool and do not ask for unrelated credentials or system-wide privileges.

Purpose & Capability

Name/description (PE/PB historical percentiles, BAND analysis, ETF premium) align with the code and SKILL.md. Declared dependencies (akshare, pandas, numpy, requests) and data sources (EastMoney/Tencent/Sina/AkShare) are appropriate for the stated purpose.

Instruction Scope

SKILL.md limits runtime behavior to fetching market and historical data, calculating percentiles, producing suggestions, and exporting JSON/CSV/Excel. It does not instruct reading unrelated system files, environment secrets, or transmitting data to unexpected endpoints beyond financial data sources.

Install Mechanism

No install spec is present (instruction-only plus included source file). Dependencies are standard Python packages listed in requirements.txt; no arbitrary downloads or extract-from-URL steps are used.

Credentials

The skill does not request any environment variables, credentials, or config paths. Its network usage (requests to public finance APIs / akshare) is proportionate to data-gathering for valuation analysis.

Persistence & Privilege

always is false and the skill does not request persistent/privileged system presence. There is no indication it modifies other skills or system-wide agent settings.

Assessment

This skill appears coherent for stock/ETF valuation: it fetches public market and historical data, computes percentiles/BANDs, and can export results. Before installing: (1) Confirm you are comfortable the agent can make outbound network requests to EastMoney/Tencent/Sina/AkShare, (2) run the code in a controlled environment if you need to restrict network/file access, (3) note akshare/openpyxl are optional—install only if you need those features, (4) review the included main.py yourself or in a sandbox if you want to verify no unexpected external endpoints or data exfiltration, and (5) pin dependency versions and run in a virtualenv to reduce supply-chain risk. The skill does not request secrets or elevated privileges.

股票和ETF估值监控SKILL，用于评估投资标的的估值水平，帮助投资者识别机会区间（低估）和风险区间（高估）。

功能特点

• 多维度估值分析：支持PE（市盈率）、PB（市净率）估值指标
• 历史百分位计算：基于5年历史数据计算PE/PB百分位
• 估值区间评估：
  • 机会区间（低估）：PE/PB百分位 < 30%
  • 合理区间：PE/PB百分位 30%-70%
  • 风险区间（高估）：PE/PB百分位 > 70%
• BAND分析：计算PE/PB的20%/50%/80%分位数作为估值区间带
• 批量查询：支持同时查询多只股票或ETF

使用方法

查询单只股票估值

查询股票 300327 的估值
查询中颖电子的估值情况
评估 300327 的投资价值

查询多只股票的估值

查询 300327、002594、600519 的估值
比较中颖电子、比亚迪、贵州茅台的估值水平

查询ETF估值

查询 ETF 510300 的估值
评估沪深300ETF的投资价值

获取估值提醒

监控 300327 的估值，低于机会区间提醒我
当 510300 进入风险区间时发出警告

输出说明

SKILL返回以下信息：

1. 基本信息：股票名称、当前价格、总市值
2. PE估值：
   • 当前PE（动态）
   • 历史PE百分位
   • PE估值区间（最低/中位数/最高）
   • PE BAND（20%/50%/80%分位）
3. PB估值：
   • 当前PB
   • 历史PB百分位
   • PB估值区间（最低/中位数/最高）
   • PB BAND（20%/50%/80%分位）
4. 估值评估：
   • 当前估值区间（机会/合理/风险）
   • 投资建议

估值区间定义

区间	PE百分位	PB百分位	投资建议
机会区间（低估）	< 30%	< 30%	积极关注，适合定投
合理区间	30%-70%	30%-70%	持有观望
风险区间（高估）	> 70%	> 70%	考虑减仓

数据源

• 实时行情数据：东方财富
• 历史财务数据：东方财富数据中心
• 历史价格数据：AkShare

注意事项

1. 数据仅供参考，不构成投资建议
2. 估值分析基于历史数据，未来表现可能不同
3. 建议结合基本面分析和市场环境综合判断
4. 部分新上市股票可能历史数据不足，百分位计算可能不准确

依赖要求

• Python 3.8+
• akshare
• pandas
• numpy
• requests

更新日志

v1.0.0 (2026-03-10)

• 初始版本发布
• 支持A股和ETF估值查询
• 支持PE/PB历史百分位计算
• 支持估值区间评估